{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aee887f7-dd0a-436f-a663-fb0cdc8a42ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测准确率:  0.8666666666666667\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "raw_df=np.loadtxt('logi-y.txt',delimiter=',',encoding='utf-8')\n",
    "data=raw_df[:,0:2]\n",
    "target = raw_df[:,2]\n",
    "x=np.array(data)\n",
    "y=np.array(target)\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1,test_size=30)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "ac=accuracy_score(y_test,model.predict(x_test))\n",
    "print(\"模型预测准确率: \",ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3cf5af78-752b-4ca0-b9f8-4f66d704e2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "N,M=500,500\n",
    "t1=np.linspace(0,100,N)\n",
    "t2=np.linspace(0,100,M)\n",
    "x1,x2=np.meshgrid(t1,t2)\n",
    "x_new=np.stack((x1.flat,x2.flat),axis=1)\n",
    "y_predict=model.predict(x_new)\n",
    "y_hat=y_predict.reshape(x1.shape)\n",
    "iris_cmap=ListedColormap(['#ACF080','#A0A0FF'])\n",
    "plt.pcolormesh(x1,x2,y_hat,cmap=iris_cmap)\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=60,c='b',marker='o')\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=60,c='r',marker='^')\n",
    "plt.rcParams['font.sans-serif']='SimHei'\n",
    "plt.xlabel('科目一成绩')\n",
    "plt.ylabel('科目二成绩')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c62374e6-050b-45da-92e5-692c02d0eeae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
